US2025200360A1PendingUtilityA1
Method and apparatus with organic molecule spectrum prediction
Assignee: SAMSUNG ELECTRONICS CO LTDPriority: Dec 13, 2023Filed: Apr 24, 2024Published: Jun 19, 2025
Est. expiryDec 13, 2043(~17.4 yrs left)· nominal 20-yr term from priority
G06N 5/04G06N 3/08
61
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Claims
Abstract
A method and apparatus with organic molecule spectrum prediction are disclosed. The method includes accessing a molecular structure representation of an organic molecule; generating parameters of an approximated Franck-Condon progression by inputting the molecular structure representation to a neural network model that infers the parameters from the molecular structure representation; and generating a spectrum of the organic molecule based on the generated parameters.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A method performed by a computing device comprising processing hardware and storage hardware, the method comprising:
storing, in the storage hardware, a representation of a molecular structure of an organic molecule; performing, by a neural network model executed by the processing hardware, inference on the representation of the molecular structure, to infer parameters of an approximated Franck-Condon progression; and generating a spectrum of the organic molecule by the processing hardware applying the inferred parameters to the approximated Franck-Condon progression.
2 . The method of claim 1 , further comprising training the neural network model based on an error between a ground truth spectrum of the representation of the molecular structure and the generated spectrum of the organic molecule.
3 . The method of claim 1 , wherein, with respect to organic molecules, the neural network model is trained after calculating a highest occupied molecular orbital (homo) energy level, a lowest unoccupied molecular orbital (lumo) energy level, and an electron transition energy level through a density functional theory (DFT).
4 . The operating method of claim 1 , wherein the processing hardware is configured to perform the following as the approximated Franck-Condon progression:
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,
where, I(x) denotes an intensity of absorption and emission of a wavelength x, E 0 denotes electron transition energy in a ground state and an excited state, S i denotes a Huang-Rhys factor that is a difference in molecular structure expressed by a specific vibrational mode, ω i denotes a vibrational energy level of a specific vibrational mode, v i denotes an energy level of a specific vibrational mode, C denotes a baseline distribution function parameter, and n denotes an approximated number of vibrational energy levels.
5 . The operating method of claim 1 , wherein the parameters comprise transition energy of the Franck-Condon progression, a baseline distribution function parameter, transition degrees, and vibrational energy levels of each vibrational mode according to the approximated number of vibrational energy levels.
6 . The operating method of claim 1 , wherein the molecular structure is in a Molfile format or a format of the simplified molecular-input line-entry system (SMILES).
7 . The operating method of claim 1 , wherein the spectrum of the organic molecule comprises an emission or absorption spectrum according to a wavelength with respect to the organic molecule.
8 . A method of training a neural network model performed by a computing device comprising processing hardware and storage hardware, the method comprising:
accessing, by the processing hardware, training data in the storage hardware, the training data comprising training samples, each training sample comprising a molecular structure representation of an organic molecule and an associated ground truth spectrum of the molecular structure; for each training sample, generating, by the processing hardware, corresponding parameters of an approximated Franck-Condon progression by inputting the molecular structure representation of the training sample to a neural network model which infers the corresponding parameters; for each training sample, generating, by the processing hardware, a corresponding spectrum of the training sample based on the corresponding obtained parameters; and for each training sample, training, by the processing hardware, the neural network model based on an error between the corresponding ground truth spectrum and the corresponding generated spectrum.
9 . The method of claim 8 , wherein the processing hardware is configured to perform the following as the approximated Franck-Condon progression:
I
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x
)
=
∑
v
1
=
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∞
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v
2
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exp
[
-
{
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∑
i
=
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n
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}
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]
,
where, I(x) denotes an intensity of absorption and emission of a wavelength x, E 0 denotes electron transition energy in a ground state and an excited state, S i denotes a Huang-Rhys factor that is a difference in molecular structure expressed by a specific vibrational mode, ω i denotes a vibrational energy level of a specific vibrational mode, v i denotes an energy level of a specific vibrational mode, C denotes a baseline distribution function parameter, and n denotes an approximated number of vibrational energy levels.
10 . The method of claim 8 , wherein the parameters comprise transition energy of the Franck-Condon progression, a baseline distribution function parameter, transition degrees, and vibrational energy levels of each vibrational mode according to an approximated number of vibrational energy levels.
11 . The method of claim 8 , wherein the obtaining of the parameters for each of the training samples comprises:
calculating a highest occupied molecular orbital (homo) energy level, a lowest unoccupied molecular orbital (lumo) energy level, and an electron transition energy level using a density functional theory (DFT) for the organic molecules.
12 . The method of claim 8 , wherein the molecular structure representation of each training sample is in a Molfile format or a format of the simplified molecular-input line-entry system (SMILES).
13 . The method of claim 1 , wherein the neural network model comprises layers of nodes with weights of connections therebetween, the layers including an input layer configured to receive the molecule structure representations and an output layer configured to output the parameters.
14 . An apparatus comprising:
one or more processors; and a memory storing instructions configured to cause the one or more processors to:
access a molecular structure representation of an organic molecule;
generate parameters of an approximated Franck-Condon progression by inputting the molecular structure representation to a neural network model that infers the parameters from the molecular structure representation; and
generate a spectrum of the organic molecule based on the generated parameters.
15 . The apparatus of claim 14 , wherein the neural network model is trained based on an error between a ground truth spectrum of the molecular structure representation and the generated spectrum.
16 . The apparatus of claim 14 , wherein, with respect to organic molecules, the neural network model is trained after calculating a highest occupied molecular orbital (homo) energy level, a lowest unoccupied molecular orbital (lumo) energy level, and an electron transition energy level through a density functional theory (DFT).
17 . The apparatus of claim 14 , wherein the instructions are further configured to cause the one or more processors to perform the following approximated Franck-Condon progression:
I
(
x
)
=
∑
v
1
=
0
∞
∑
v
2
=
0
∞
…
∑
v
n
=
0
∞
(
E
0
-
∑
i
=
1
n
v
i
ω
i
E
0
)
3
∏
i
=
1
n
(
S
i
v
i
e
-
S
i
v
i
!
)
exp
[
-
{
x
-
(
E
0
-
∑
i
=
1
n
v
i
ω
i
)
}
2
2
C
2
]
,
where, I(x) denotes an intensity of absorption and emission of a wavelength x, E 0 denotes electron transition energy in a ground state and an excited state, S i denotes a Huang-Rhys factor that is a difference in molecular structure expressed by a specific vibrational mode, ω i denotes a vibrational energy level of a specific vibrational mode, v i denotes an energy level of a specific vibrational mode, C denotes a baseline distribution function parameter, and n denotes an approximated number of vibrational energy levels.
18 . The apparatus of claim 14 , wherein the parameters comprise transition energy of the Franck-Condon progression, a baseline distribution function parameter, transition degrees and vibrational energy levels of each vibrational mode according to an approximated number of vibrational energy levels.
19 . The apparatus of claim 14 , wherein the molecular structure comprises representation may be in a Molfile format or in a format of the simplified molecular-input line-entry system (SMILES).
20 . The apparatus of claim 14 , wherein the generated spectrum comprises an emission or absorption spectrum according to a wavelength with respect to the organic molecule.Join the waitlist — get patent alerts
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